code: def test_value_cuda(self, input_param, expected_h, expected_c):
      device = (
          torch.device("cuda:0")
          if torch.cuda.is_available()
          else torch.device("cpu:0")
      )
      torch.manual_seed(0)
      x = torch.randn(4, 32).to(device)
      h = torch.randn(4, 2).to(device)
      c = torch.randn(4, 2).to(device)
      lltm = LLTM(**input_param).to(device)
      new_h, new_c = lltm(x, (h, c))
      (new_h.sum() + new_c.sum()).backward()
  
      assert_allclose(
          new_h, expected_h.to(device), rtol=_rtol, atol=0.001
      )
      assert_allclose(
          new_c, expected_c.to(device), rtol=_rtol, atol=0.001
      )
  
file_path: MONAI/tests/test_lltm.py
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.networks.layers import LLTM
  from tests.utils import SkipIfNoModule, assert_allclose, is_tf32_env
label_desc: Use image node_id 2 for calling the TestLLTM obj's underlying member method code with example usage: obj.test_value_cuda(input_param, expected_h, expected_c) without return types